However, the existing local models did not fully explore the potential of the local low-rank assumption for the following three aspects. First, the existing studies [1]}, [2]}, [3]}, [4]}, [5]} did not fully discover diverse locality. Adopting a relatively broad coverage of users in the local model [1]}, [2]}, [3]}, the size of the local models is close to that of the global model, thereby violating the intuition of local models. Although [4]}, [5]} employed the local models with small sub-communities, their performance was often worse than that of the global model owing to the lack of training data. Second, existing local models did not attempt to train and combine a large number of local models (i.e., at most 100 models) to capture smaller and more coherent sub-communities. Thus, the previous studies can be regarded as developing a small number of relatively similar models; their performance improvement is most likely due to the ensemble effect, similar to bagging [11]}. Lastly, the base models for learning the local models were often limited to linear latent factor models, which made it difficult to identify meaningful non-linear patterns in local models.
